AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
Engineer focusing on providing optimal user and agent experiences on the LanceDB platform.
Takes high-level direction (e.g., “identify where UX is significant ahead of AX and drive more parity”) and drives to results
Success looks like a LanceDB platform that incorporates compelling user and agent experiences for exploring and managing their multimodal data, enabling more adoption and increased usage.
Work with the rest of engineering to optimize user and agent experience across their parts of the platform.
Build scalable backends for LanceDB Enterprise to support user and agent experiences across billion-scale datasets.
Our tech stack: Rust, Typescript, React, Python, k8s, Terraform, CSPs
8+ years as a software engineer
Experience working on frontend and backend parts of large-scale data platforms (e.g., databases, data infra, ML/AI systems), including work on concurrency and multitenancy
Experience crafting UIs using Typescript, React/next.js/Vercel, CLIs, and APIs
Experience building elegant user-oriented and agent-oriented experiences
Demonstrated ownership of ambiguous projects with minimal guidance
Strong written and verbal communication (can drive alignment across teams)
Comfortable using data (metrics, experiments, usage) to guide decisions
Experience contributing to or working with open source communities
Job details are sourced from the employer's original posting.
Open job postingAbout the company
LanceDB is an open-source vector database that allows you to build AI applications with a focus on performance and ease of use. It is designed for efficient storage and retrieval of vector embeddings.